################################################
# Clean replication code for Tandeo 2022: 2SLS Analysis
# Created AJH
# Created 6/13/2022
# Last edited 6/14/2022
################################################
library(tidyverse)
library(aod)
library(stargazer)
library(magrittr)
load("chapter_5_rewarding_responsiveness/db_precinct.Rdata")
# source code to calculate conley standard errors
source("chapter_5_rewarding_responsiveness/conley-se-master/code/conley.R")

# create database of only election years
elec <- db %>% 
  filter(year %in% 
           c("2006", "2009", "2012", "2015", "2018","2021"))




##################################################################
#  Comparing the first stage using t, t-1, and both
##################################################################
# model 1- just t
model1 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std 
               |year + cve_secc | (tandeo ~ d2z_std_dryness_std)|lat + lon, data = elec,keepCX=TRUE)

# Conley SEs for first stage
SE_model1_1s <-ConleySEs(reg = model1$stage1,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_model1_1s <- SE_model1_1s$Spatial
f_model1_1s <- wald.test(b = coef(model1$stage1), Sigma = vcov_model1_1s, Terms =4)$result$chi2[1] %>% round(2)
ses_model1_1s  <- sapply(abs(SE_model1_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_model1 <-ConleySEs(reg = model1,
                      unit = "cve_secc", 
                      time = "year",
                      lat = "lat", lon = "lon",
                      dist_fn = "SH",
                      dist_cutoff = 60, 
                      lag_cutoff = 1,
                      cores = 1, 
                      verbose = FALSE) 
vcov_model1 <- SE_model1$Spatial
ses_model1  <- sapply(abs(SE_model1$Spatial), sqrt) %>% round(3)
# reduced form 
rf_model1 <- felm(ivs_jd ~ d2z_std_dryness_std  + wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std |year + cve_secc |0|lat + lon, data = elec,keepCX=TRUE)
# Conley SEs
SE_rf_model1 <-ConleySEs(reg = rf_model1,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_rf_model1 <- SE_rf_model1$Spatial
ses_rf_model1 <- sapply(abs(SE_rf_model1$Spatial), sqrt) %>% round(3)
fvector_model1 <- c("First stage F on Excluded Instruments (Conley adj.)", f_model1_1s, "", "")

stargazer(model1$stage1,  rf_model1,model1, 
          type= "text",
          add.lines=list(fvector_model1),
          se = list(ses_model1_1s,ses_rf_model1,ses_model1),
          keep.stat = c("n"),
          no.space = TRUE,
          column.labels = c("First Stage","Reduced Form","2sls"),
          dep.var.labels.include = FALSE,
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Support for Local Mayor",
          notes = c("Year and precinct fixed effects", "Conley standard errors (60 km)" ),
          order = c("tandeo", "d2z_std_dryness_std", "wealth_index_dryness_std", 
                    "flood_risk_dryness_std", "green_space_std_dryness_std" ),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)", "Wealth index x dryness (t)", 
                                "Flood risk x dryness (t)", "Green space x dryness (t)"),
          digits=3,
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/comparing_2sls/model1.tex")



# Model 2: just t-1

model2 <- felm(ivs_jd ~ wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std 
               |year + cve_secc | (tandeo ~ d2z_std_lag1_dryness_std)|lat + lon, data = elec,keepCX=TRUE)

# Conley SEs for first stage
SE_model2_1s <-ConleySEs(reg = model2$stage1,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_model2_1s <- SE_model2_1s$Spatial
f_model2_1s <- wald.test(b = coef(model2$stage1), Sigma = vcov_model2_1s, Terms =4)$result$chi2[1] %>% round(2)
ses_model2_1s  <- sapply(abs(SE_model2_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_model2 <-ConleySEs(reg = model2,
                      unit = "cve_secc", 
                      time = "year",
                      lat = "lat", lon = "lon",
                      dist_fn = "SH",
                      dist_cutoff = 60, 
                      lag_cutoff = 1,
                      cores = 1, 
                      verbose = FALSE) 
vcov_model2 <- SE_model2$Spatial
ses_model2  <- sapply(abs(SE_model2$Spatial), sqrt) %>% round(3)
# reduced form 
rf_model2 <- felm(ivs_jd ~ d2z_std_lag1_dryness_std  + wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std |year + cve_secc |0|lat + lon, data = elec,keepCX=TRUE)
# Conley SEs
SE_rf_model2 <-ConleySEs(reg = rf_model2,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_rf_model2 <- SE_rf_model2$Spatial
ses_rf_model2 <- sapply(abs(SE_rf_model2$Spatial), sqrt) %>% round(3)
fvector_model2 <- c("First stage F on Excluded Instruments (Conley adj.)", f_model2_1s, "", "")



###############
# Primary Spec 
# Instruments are distance to center in t and t-1 (d2z_t, lag1_d2z)
###############
model3 <- felm(ivs_jd ~ wealth_index_dryness_std  + wealth_index_lag1_dryness_std +
                 flood_risk_dryness_std + flood_risk_lag1_dryness_std +
                 green_space_std_lag1_dryness_std +green_space_std_dryness_std 
               |year + cve_secc | 
                 (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std)|lat + lon, data = elec,keepCX=TRUE)

# Conley SEs for first stage
SE_model3_1s <-ConleySEs(reg = model3$stage1,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_model3_1s <- SE_model3_1s$Spatial
f_model3_1s <- wald.test(b = coef(model3$stage1), Sigma = vcov_model3_1s, Terms =7:8)$result$chi2[1] %>% round(2)
ses_model3_1s  <- sapply(abs(SE_model3_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_model3 <-ConleySEs(reg = model3,
                      unit = "cve_secc", 
                      time = "year",
                      lat = "lat", lon = "lon",
                      dist_fn = "SH",
                      dist_cutoff = 60, 
                      lag_cutoff = 1,
                      cores = 1, 
                      verbose = FALSE) 
vcov_model3 <- SE_model3$Spatial
ses_model3  <- sapply(abs(SE_model3$Spatial), sqrt) %>% round(3)
# reduced form 
rf_model3 <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std  + 
                    wealth_index_dryness_std + wealth_index_lag1_dryness_std + 
                    flood_risk_dryness_std  + flood_risk_lag1_dryness_std + 
                    green_space_std_dryness_std + green_space_std_lag1_dryness_std |year + cve_secc |0|lat + lon, data = elec,keepCX=TRUE)
# Conley SEs
SE_rf_model3 <-ConleySEs(reg = rf_model3,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_rf_model3 <- SE_rf_model3$Spatial
ses_rf_model3 <- sapply(abs(SE_rf_model3$Spatial), sqrt) %>% round(3)
fvector_model3 <- c("First stage F on Excluded Instruments (Conley adj.)", f_model3_1s, "", "")

stargazer(model3$stage1,  rf_model3,model3, 
          type= "text",
          add.lines=list(fvector_model3),
          se = list(ses_model3_1s,ses_rf_model3,ses_model3),
          keep.stat = c("n"),
          no.space = TRUE,
          column.labels = c("First Stage","Reduced Form","2sls"),
          dep.var.labels.include = FALSE,
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Support for Local Mayor",
          notes = c("Year and precinct fixed effects", "Conley standard errors (60 km)" ),
          keep = c("tandeo", "d2z_std_dryness_std", "d2z_std_lag1_dryness_std"),
          order = c("tandeo", "d2z_std_dryness_std",  "d2z_std_lag1_dryness_std" ),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)", "Dist to center x dryness (t-1)"
                               ),
          digits=3,
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/mainspec.tex")

# Appendix: Main spec with full controls (same model, but putting everything in the table)
stargazer(model3$stage1,  rf_model3,model3, 
          type= "text",
          add.lines=list(fvector_model3),
          se = list(ses_model3_1s,ses_rf_model3,ses_model3),
          keep.stat = c("n"),
          no.space = TRUE,
          column.labels = c("First Stage","Reduced Form","2sls"),
          dep.var.labels.include = FALSE,
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Support for Local Mayor",
          notes = c("Year and precinct fixed effects", "Conley standard errors (60 km)" ),
          order = c("tandeo",
                    "d2z_std_dryness_std",  "d2z_std_lag1_dryness_std", 
                    "wealth_index_dryness_std", "wealth_index_lag1_dryness_std", 
                    "flood_risk_dryness_std", "flood_risk_lag1_dryness_std", "
                    green_space_lag1_std_dryness_std", "green_space_lag1_std_dryness_std" ),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)", "Dist to center x dryness (t-1)",
                                "Wealth index x dryness (t)", "Wealth index x dryness (t-1)", 
                                "Flood risk x dryness (t)", "Flood risk x dryness (t-1)", 
                                "Green space x dryness (t)", "Green space x dryness (t-1)"),
          digits=3,
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/mainspec_show_controls.tex")


# Appendix: Table with each of the three different first stages (t, t-1, t and t-1)
fvector_1s <- c("First stage F on Excluded Instruments (Conley adj.)",f_model1_1s, f_model2_1s, f_model3_1s) 
stargazer(model1$stage1, model2$stage1, model3$stage1,
          se = list(ses_model1_1s,ses_model2_1s,ses_model3_1s),
          add.lines = list(fvector_1s),
          type ="text",
          keep.stat = c("n"),
          no.space = TRUE,
          dep.var.labels.include = FALSE,
          dep.var.caption = "Ration List",
          model.numbers = FALSE,
          title = "Comparing First Stages Using Different Time Periods' Rain",
          notes = c("Year and precinct fixed effects", "Conley standard errors (60 km)") ,
          digits=3,
          order = c("d2z_std_dryness_std","d2z_std_lag1_dryness_std",
                    "wealth_index_dryness_std", "wealth_index_lag1_dryness_std",
                    "flood_risk_dryness_std", "flood_risk_lag1_dryness_std",
                    "green_space_std_dryness_std", "flood_risk_lag1_dryness_std"),
          covariate.labels =  c( "Dist to center x dryness (t)",
                                 "Dist to center x dryness (t-1)",
                                 "Wealth index x dryness (t)", 
                                 "Wealth index x dryness (t-1)", 
                                 "Flood risk x dryness (t)",
                                 "Flood risk x dryness (t-1)",
                                 "Green space x dryness (t)",
                                 "Green space x dryness (t-1)"),
          label="tab:comparing_instruments",
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/comparing_instruments.tex")

### Model 4: Main spec results without controls 
model4 <- felm(ivs_jd ~ 0 |year + cve_secc | 
                 (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std)|lat + lon, data = elec,keepCX=TRUE)

# Conley SEs for first stage
SE_model4_1s <-ConleySEs(reg = model4$stage1,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_model4_1s <- SE_model4_1s$Spatial
f_model4_1s <- wald.test(b = coef(model4$stage1), Sigma = vcov_model4_1s, Terms =1:2)$result$chi2[1] %>% round(2)
ses_model4_1s  <- sapply(abs(SE_model4_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_model4 <-ConleySEs(reg = model4,
                      unit = "cve_secc", 
                      time = "year",
                      lat = "lat", lon = "lon",
                      dist_fn = "SH",
                      dist_cutoff = 60, 
                      lag_cutoff = 1,
                      cores = 1, 
                      verbose = FALSE) 
vcov_model4 <- SE_model4$Spatial
ses_model4  <- sapply(abs(SE_model4$Spatial), sqrt) %>% round(3)
# reduced form 
rf_model4 <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std  |year + cve_secc |0|lat + lon, data = elec,keepCX=TRUE)
# Conley SEs
SE_rf_model4 <-ConleySEs(reg = rf_model4,
                         unit = "cve_secc", 
                         time = "year",
                         lat = "lat", lon = "lon",
                         dist_fn = "SH",
                         dist_cutoff = 60, 
                         lag_cutoff = 1,
                         cores = 1, 
                         verbose = FALSE) 
vcov_rf_model4 <- SE_rf_model4$Spatial
ses_rf_model4 <- sapply(abs(SE_rf_model4$Spatial), sqrt) %>% round(3)
fvector_model4 <- c("First stage F on Excluded Instruments (Conley adj.)", f_model4_1s, "", "")

stargazer(model4$stage1,  rf_model4,model4, 
          type= "text",
          add.lines=list(fvector_model4),
          se = list(ses_model4_1s,ses_rf_model4,ses_model4),
          keep.stat = c("n"),
          no.space = TRUE,
          column.labels = c("First Stage","Reduced Form","2sls"),
          dep.var.labels.include = FALSE,
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Support for Local Mayor",
          notes = c("Year and precinct fixed effects", "Conley standard errors (60 km)" ),
          order = c("tandeo",
                    "d2z_std_dryness_std",  "d2z_std_lag1_dryness_std" ),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)", "Dist to center x dryness (t-1)"),
          digits=3,
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/mainspec_without_controls.tex")

##################
# Robustness 1: Unbalanced panel that drops observations after the first year of inclusion on rationing lists
    # create variables for first year rationed
    first_year_rationed <- db %>%
      filter(tandeo==1) %>% 
      group_by(cve_secc) %>% 
      summarize(first_year_rationed = min(year)) %>% 
      ungroup()
    
    secciones <-db %>% 
      group_by(cve_secc) %>%
      slice(1) %>%
      select(cve_secc) %>% 
      ungroup()


    secciones <- left_join(secciones, first_year_rationed, by = "cve_secc")
    secciones$first_year_rationed <- ifelse(is.na(secciones$first_year_rationed), 2022, secciones$first_year_rationed)

    db <- left_join(db, secciones, by ="cve_secc")



    db_r1 <- db %>% filter(year <=first_year_rationed) %>% 
    filter(year %in% c("2006", "2009", "2012", "2015", "2018","2021"))

# Model R1: 
modelr1 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                  wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std)|lat + lon, data = db_r1,keepCX=TRUE)

# Conley SEs for first stage
SE_modelr1_1s <-ConleySEs(reg = modelr1$stage1,
                          unit = "cve_secc", 
                          time = "year",
                          lat = "lat", lon = "lon",
                          dist_fn = "SH",
                          dist_cutoff = 60, 
                          lag_cutoff = 1,
                          cores = 1, 
                          verbose = FALSE) 
vcov_modelr1_1s <- SE_modelr1_1s$Spatial
f_modelr1_1s <- wald.test(b = coef(modelr1$stage1), Sigma = vcov_modelr1_1s, Terms =7:8)$result$chi2[1] %>% round(2)
fvector_modelr1 <- c("First stage F on Excluded Instruments", f_modelr1_1s, "", "")

ses_modelr1_1s  <- sapply(abs(SE_modelr1_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_modelr1 <-ConleySEs(reg = modelr1,
                       unit = "cve_secc", 
                       time = "year",
                       lat = "lat", lon = "lon",
                       dist_fn = "SH",
                       dist_cutoff = 60, 
                       lag_cutoff = 1,
                       cores = 1, 
                       verbose = FALSE) 
vcov_modelr1 <- SE_modelr1$Spatial
ses_modelr1  <- sapply(abs(SE_modelr1$Spatial), sqrt) %>% round(3)

# reduced form 
rf_modelr1 <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std +wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                     wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc |0|lat + lon, data = db_r1,keepCX=TRUE)
# Conley SEs
SE_rf_modelr1 <-ConleySEs(reg = rf_modelr1,
                          unit = "cve_secc", 
                          time = "year",
                          lat = "lat", lon = "lon",
                          dist_fn = "SH",
                          dist_cutoff = 60, 
                          lag_cutoff = 1,
                          cores = 1, 
                          verbose = FALSE) 
vcov_rf_modelr1 <- SE_rf_modelr1$Spatial
ses_rf_modelr1 <- sapply(abs(SE_rf_modelr1$Spatial), sqrt) %>% round(3)

# reduced form 
rf_modelr1 <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std +alt_std_dryness_std+alt_std_lag1_dryness_std+ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                     wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc |0|lat + lon, data = db_r1,keepCX=TRUE)
# Conley SEs
SE_rf_modelr1 <-ConleySEs(reg = rf_modelr1,
                          unit = "cve_secc", 
                          time = "year",
                          lat = "lat", lon = "lon",
                          dist_fn = "SH",
                          dist_cutoff = 60, 
                          lag_cutoff = 1,
                          cores = 1, 
                          verbose = FALSE) 
vcov_rf_modelr1 <- SE_rf_modelr1$Spatial
ses_rf_modelr1 <- sapply(abs(SE_rf_modelr1$Spatial), sqrt) %>% round(3)

stargazer(modelr1$stage1,  rf_modelr1,modelr1, 
          type= "text",
          add.lines= list(fvector_modelr1,c("Controls", "Yes", "Yes", "Yes")),
          se = list(ses_modelr1_1s,ses_rf_modelr1,ses_modelr1),
          keep.stat = c("n"),
          no.space = TRUE,
          dep.var.labels = c("Rationing","Incumbent Party Vote Share"),
          column.labels = c("First Stage","Reduced Form","2sls"),
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Unbalanced Panel Where Observations are Dropped One Year After Treatment",
          notes = c("Year and precinct fixed effects", "Conley standard errors (10 km)", "Controls for green space, flood risk" ,"and wealth interacted with dryness" ),
          keep = c("tandeo", "d2z_std_dryness_std","d2z_std_lag1_dryness_std"),
          order = c("tandeo", "d2z_std_dryness_std","d2z_std_lag1_dryness_std"),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)",
                                "Dist to center x dryness (t-1)",
                                "Wealth index x dryness (t)", 
                                "Wealth index x dryness (t-1)", 
                                "Flood risk x dryness (t)",
                                "Flood risk x dryness (t-1)",
                                "Green space x dryness (t)",
                                "Green space x dryness (t-1)"),
          digits=3,
          label = "tab:modelr1",
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/modelr1.tex")

################
# 2: Balanced panel, in which once a neighborhood has appeared on the rationing list, it is considered treated in all subsequent years   
################

db_r2 <- db %>% 
  filter(year %in% c("2006", "2009", "2012", "2015", "2018","2021"))
db_r2$tandeo <- ifelse(db_r2$year >= db_r2$first_year_rationed, 1, db$tandeo)

# Model r2: 
modelr2 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                  wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std )|lat + lon, data = db_r2,keepCX=TRUE)

# Conley SEs for first stage
SE_modelr2_1s <-ConleySEs(reg = modelr2$stage1,
                          unit = "cve_secc", 
                          time = "year",
                          lat = "lat", lon = "lon",
                          dist_fn = "SH",
                          dist_cutoff = 60, 
                          lag_cutoff = 1,
                          cores = 1, 
                          verbose = FALSE) 
vcov_modelr2_1s <- SE_modelr2_1s$Spatial
f_modelr2_1s <- wald.test(b = coef(modelr2$stage1), Sigma = vcov_modelr2_1s, Terms =7:8)$result$chi2[1] %>% round(2)
fvector_modelr2 <- c("First stage F on Excluded Instruments", f_modelr2_1s, "", "")

ses_modelr2_1s  <- sapply(abs(SE_modelr2_1s$Spatial), sqrt) %>% round(3)
# standard errors for 2sls
SE_modelr2 <-ConleySEs(reg = modelr2,
                       unit = "cve_secc", 
                       time = "year",
                       lat = "lat", lon = "lon",
                       dist_fn = "SH",
                       dist_cutoff = 60, 
                       lag_cutoff = 1,
                       cores = 1, 
                       verbose = FALSE) 
vcov_modelr2 <- SE_modelr2$Spatial
ses_modelr2  <- sapply(abs(SE_modelr2$Spatial), sqrt) %>% round(3)



# reduced form 
rf_modelr2 <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std + wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                     wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc |0|lat + lon, data = elec,keepCX=TRUE)
# Conley SEs
SE_rf_modelr2 <-ConleySEs(reg = rf_modelr2,
                          unit = "cve_secc", 
                          time = "year",
                          lat = "lat", lon = "lon",
                          dist_fn = "SH",
                          dist_cutoff = 60, 
                          lag_cutoff = 1,
                          cores = 1, 
                          verbose = FALSE) 
vcov_rf_modelr2 <- SE_rf_modelr2$Spatial
ses_rf_modelr2 <- sapply(abs(SE_rf_modelr2$Spatial), sqrt) %>% round(3)


stargazer(modelr2$stage1,  rf_modelr2,modelr2, 
          type= "text",
          add.lines= list(fvector_modelr2,c("Controls", "Yes", "Yes", "Yes")),
          se = list(ses_modelr2_1s,ses_rf_modelr2,ses_modelr2),
          keep.stat = c("n"),
          no.space = TRUE,
          dep.var.labels = c("Rationing","Incumbent Party Vote Share"),
          column.labels = c("First Stage","Reduced Form","2sls"),
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Balanced Panel Where Observations are Counted As Treated Every Year After First Inclusion on Rationing List",
          notes = c("Year and precinct fixed effects", "Conley standard errors (10 km)", "Controls for green space, flood risk" ,"and wealth interacted with dryness" ),
          keep = c("tandeo", "d2z_std_dryness_std","d2z_std_lag1_dryness_std"),
          order = c("tandeo", "d2z_std_dryness_std","d2z_std_lag1_dryness_std"),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)",
                                "Dist to center x dryness (t-1)",
                                "Wealth index x dryness (t)", 
                                "Wealth index x dryness (t-1)", 
                                "Flood risk x dryness (t)",
                                "Flood risk x dryness (t-1)",
                                "Green space x dryness (t)",
                                "Green space x dryness (t-1)"),
          digits=3,
          label = "tab:modelr2",
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/modelr2.tex")

rm(list=setdiff(ls(), c("db","elec", "Val_XeeXhC", "ConleySEs", "DistMat", "TimeDist", "XeeXhC", "XeeXhC_Lg")))

# Main spec with precicnt-clustered standard errors
model2 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                 wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std)|cve_secc, data = elec)
model2_rf <- felm(ivs_jd ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std + wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                    wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | 0|cve_secc, data = elec)
f_model2_1s <- wald.test(b = coef(model2$stage1), Sigma = vcov(model2$stage1), Terms =7:8)$result$chi2[1] %>% round(2)
fvector_model2 <- c("First stage F on Excluded Instruments", f_model2_1s, "", "")

stargazer(model2$stage1,  model2_rf,model2, 
          type= "text",
          add.lines = list(fvector_model2),
          keep.stat = c("n"),
          no.space = TRUE,
          column.labels = c("First Stage","Reduced Form","2sls"),
          dep.var.labels.include = FALSE,
          model.numbers          = FALSE,
          dep.var.caption = "",
          title = "Support for Local Mayor: 2sls Results with Controls (Using Distance to Center as Instrument)",
          notes = c("Year and precinct fixed effects", "Standard errors clustered by precinct" ),
          order = c("tandeo", "d2z_std_dryness_std","d2z_std_lag1_dryness_std",
                    "wealth_index_dryness_std", "wealth_index_lag1_dryness_std",
                    "flood_risk_dryness_std", "flood_risk_lag1_dryness_std",
                    "green_space_std_dryness_std", "flood_risk_lag1_dryness_std"),
          covariate.labels =  c("Rationing (Instrumented)",
                                "Dist to center x dryness (t)",
                                "Dist to center x dryness (t-1)",
                                "Wealth index x dryness (t)", 
                                "Wealth index x dryness (t-1)", 
                                "Flood risk x dryness (t)",
                                "Flood risk x dryness (t-1)",
                                "Green space x dryness (t)",
                                "Green space x dryness (t-1)"),
          digits=3,
          out = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/3_Tables/4_Spring_2022/final_2sls/clustered.tex")




###############################################################
# Simulating the results using different Conley distance cutoffs 
# Basically, here we want to calculate the se's for the 2sls result using a variety of distance cutoffs, 
# and plot their distribution as it compares to clustered standard errors. I use the main spec
###############################################################

model3 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                 wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std)|lat + lon, data = elec,keepCX=TRUE)

get_se_by_dist_cutoff <- function(dist){
  # standard errors for 2sls
  SE_model3 <-ConleySEs(reg = model3,
                        unit = "cve_secc", 
                        time = "year",
                        lat = "lat", lon = "lon",
                        dist_fn = "SH",
                        dist_cutoff = dist, 
                        lag_cutoff = 1,
                        cores = 1, 
                        verbose = FALSE) 
  vcov_model3 <- SE_model3$Spatial
  ses_model3  <- sapply(abs(SE_model3$Spatial), sqrt) %>% round(3)
  return(ses_model3[7])
}
# create index of values for the distance cutoff
distances <- seq(5,65, 5)
conley_ses_by_dist <- lapply(distances, get_se_by_dist_cutoff) 

# get clustered se
model4 <- felm(ivs_jd ~ wealth_index_dryness_std + flood_risk_dryness_std + green_space_std_dryness_std +
                 wealth_index_lag1_dryness_std + flood_risk_lag1_dryness_std + green_space_std_lag1_dryness_std|year + cve_secc | (tandeo ~ d2z_std_dryness_std + d2z_std_lag1_dryness_std )|cve_secc, data = elec)
clustered_se <-model4$se[7] 

p1 <-    ggplot()+ 
  geom_point(aes(x= unlist(distances), y = unlist(conley_ses_by_dist))) +
  theme_bw()+
  geom_point(aes(x= 25,y=clustered_se), color ="grey70")+ 
  geom_text(aes(x= 35,y=clustered_se), color ="grey50", label = "Neighborhood \n  clustered", size =3.5)+
  geom_point(aes(x= 60,y=unlist(conley_ses_by_dist)[12]), color ="grey50")+
  geom_text(aes(x= 60,y=0.035), color ="grey50", label = "Results \n in paper", size =3.5)+
  labs(x= "Distance (KM)", 
       y ="Std. Error on Rationing",
       title = "Variation in  Conley Standard Errors \n Using Different Distance Cutoffs", 
       note= "Plots the Conley Standard Error using Different distance cutoffs")
ggsave(p1, file = "/Users/alyssahuberts/Dropbox/2_mx_water/1_Tandeo/3_Output/1_Plots/5_Spring2022/graphics/conley_by_dist.jpg", width = 8)


#####
